Physiologically based pharmacokinetic modelling and in vivo [I]/Ki accurately predict P-glycoproteinmediated drug-drug interactions with dabigatran etexilate

Background and purpose

In vitro inhibitory potency (K_i)-based predictions of P-glycoprotein (P-gp)-mediated drug-drug interactions (DDIs) are hampered by the substantial variability in inhibitory potency. In this study, in vivo-based [I]/K_i values were used to predict the DDI risks of a P-gp substrate dabigatran etexilate (DABE) using physiologically based pharmacokinetic (PBPK) modelling.

Experimental approach

A baseline PBPK model was established with digoxin, a known P-gp substrate. The K_m (P-gp transport) of digoxin in the baseline PBPK model was adjusted to K_mⁱ to fit the change of digoxin pharmacokinetics in the presence of a P-gp inhibitor. Then ‘in vivo’ [I]/K_i of this P-gp inhibitor was calculated using K_mⁱ/K_m. Baseline PBPK model was developed for DABE, and the ‘in vivo’ [I]/K_i was incorporated into this model to simulate the static effect of P-gp inhibitor on DABE pharmacokinetics. This approach was verified by comparing the observed and the simulated DABE pharmacokinetics in the presence of five different P-gp inhibitors.

Key results

This approach accurately predicted the effects of five P-gp inhibitors on DABE pharmacokinetics (98–133% and 89–104% for the ratios of AUC and C_max respectively). The effects of 16 other P-gp inhibitors on the pharmacokinetics of DABE were also confidently simulated.

Conclusions and implications

‘In vivo’ [I]/K_i and PBPK modelling, used in combination, can accurately predict P-gp-mediated DDIs. The described framework provides a mechanistic basis for the proper design of clinical DDI studies, as well as avoiding unnecessary clinical DDI studies.